Insurance Recruiting and Agency Territory Planning

Challenge

Insurance recruiting and territory planning both depend on pattern recognition. Which applicants look like people who have succeeded before? Where should agencies be placed so the team can reach the right communities with the right local presence?

The company wanted to make those decisions with more evidence and less guesswork.

Approach

For recruiting, I looked at resumes from successful agents and searched for patterns that could help score future candidates. Prior careers mattered. Teachers, truck drivers, and other roles can signal traits that translate into insurance sales: trust, persistence, communication, community ties, and comfort working independently.

For agency planning, I combined drive-time analysis with US Census data. The goal was to identify areas with dense Hispanic and Latino populations where local agencies staffed by people from the community could build trust and connect around family protection needs.

Solution

The recruiting workflow treated resumes as structured evidence, not just documents to read one at a time. The territory workflow used isochrones to estimate reachable areas around potential agency locations, then layered demographic data on top so the business could compare markets more clearly.

Results

The result was a better decision process. AI can now improve the same kind of workflow by extracting resume features, explaining candidate fit, summarizing tradeoffs between territories, and producing maps or briefs for leadership review. The important part is that the model supports the decision. It does not replace the business context behind it.